The detection or determination of the coefficient of friction being effective between the tire and the pavement or the detection of the state of the pavement (e.g., dry, wet, snow-covered and icy), from which state of the pavement the coefficient-of-friction group can be derived, is an important prerequisite for assisting the driver with his or her driving task and thus for avoiding serious accidents or lessening the consequences thereof. In general, the assessment of road conditions resides with the driver, who adapts his or her driving behavior to said road conditions. Vehicle control systems, such as ESC (Electronic Stability Control)/TCS (Traction Control System) or ABS (Anti-lock Braking System), assist the driver in stabilizing the vehicle in the limit range so that the driver can perform his or her driving task in extreme situations more easily.
With an increasing degree of driver assistance automation through to highly automated or autonomous driving, the importance of information on the state of the pavement or on the coefficient of friction is increasing continuously. The information on the state of the pavement or on the coefficient of friction is typically used to                inform the driver        warn the driver        fix the instants of intervening in the braking system and the steering gear with driver assistance systems and        adjust vehicle control functions (e.g., brake, steering gear).        
In driver assistance systems, the avoidance of accidents is getting more and more important. Emergency braking systems (and the recently introduced emergency steering systems) make an important contribution thereto. However, the effect of such systems decisively depends on the coefficient of friction of the ground. Moisture, snow and ice considerably reduce the coefficient of friction available between the tire and the pavement as against the coefficient of friction available on a dry pavement.
EP 792 228 B1 shows a system for driving stability control for ESP (Electronic Stability Program)/ESC control systems, by means of which system a coefficient of friction can be determined in special situations. When at least one wheel utilizes the coefficient of friction (e.g., when driving on slippery ground), the vehicle brake control system can determine the coefficient of friction from the rotation behavior of the wheels and from the ESP/ESC acceleration sensors.
DE 102 56 726 A1 shows a method for generating a signal depending on the condition of the pavement using a reflection signal sensor, e.g., a radar sensor or an optical sensor, thereby making an anticipatory detection of the state of the pavement in a motor vehicle possible.
DE 10 2004 018 088 A1 shows a pavement detection system with a temperature sensor, an ultrasonic sensor and a camera. The pavement data received from the sensors are filtered and compared with reference data in order to determine the practicability of the pavement, wherein the pavement surface (e.g., concrete, asphalt, dirt, grass, sand, or gravel) and the state thereof (e.g., dry, icy, snow-covered, wet) can be classified.
DE 10 2004 047 914 A1 shows a method for assessing the state of the pavement, in which data received from several different sensors (e.g., camera, infrared sensor, rain sensor, or microphone) are merged in order to reach a state-of-pavement classification which a coefficient of friction can be assigned to.
DE 10 2008 047 750 A1 shows a determination of an adhesion coefficient with few sensors, in which torsional oscillations of a wheel of a vehicle are analyzed and a coefficient of friction is estimated on the basis of this analysis.
DE 10 2009 041 566 A1 shows a method for determining a pavement coefficient of friction μ, in which a first coefficient-of-friction parameter, which is constantly updated, and a second coefficient-of-friction parameter, which is updated situationally only, are combined with each other in order to obtain a common estimated friction value.
WO 2011/007015 A1 shows a laser-based method for coefficient-of-friction classification in motor vehicles. To this end, signals of a lidar sensor/CV sensor directed toward the pavement surface are analyzed. After that, a coefficient of friction is assigned, particularly on the basis of the amplitude of the measured pavement surface. For example, one can estimate whether snow, asphalt or ice form the pavement surface.
WO 2012/110030 A2 shows a method and a device for coefficient-of-friction estimation by means of a 3D camera, e.g., a stereo camera. At least one image of the surroundings of the vehicle is acquired by means of the 3D camera. From the image data of the 3D camera, a height profile of the road surface is created in the entire area extending in front of the vehicle. The expectable local coefficient of friction of the road surface in the area extending in front of the vehicle is estimated from the height profile.
The automatic acquisition of the state-of-pavement information is a key element on the way to the realization of autonomous driving in future.
However, the known methods are disadvantageous. On the one hand, the availability of information is highly limited (ESC). On the other hand, the sensors and algorithms are not sufficiently precise yet (camera, IR sensors, radar) or the robustness of the system is highly insufficient for safety systems (analysis of torsional oscillations of wheel, stereo camera).
The approach of the inventive solution consists in the following considerations: The coefficient-of-friction information determined according to the state of the art is usually not valid for every pavement segment.
Directly measuring systems are capable of measuring very precisely, but they are not capable of operating in an anticipatory manner. Typical examples of such systems are vehicle control systems, such as ESC, ABS or TCS, which virtually determine the coefficient of friction directly in the footprint of the tire on the basis of the slipping and running-in behavior on the tire. Technology-specifically, optical sensors (e.g., near infrared) also have a very limited capability to deliver information in a sufficiently anticipatory manner since the angle relative to the pavement must not become too acute. Both systems and also wheel speed analysis are only capable of determining the state of the pavement locally.
Other systems, particularly camera/video systems, are only capable of determining the state of the pavement indirectly (e.g., by means of classification) and are therefore less precise than directly measuring systems for process-related reasons. However, systems having a coverage depth of some/several meters (e.g., 1 m-20 m, 2 m-100 m, 5 m-200 m depending on the design of the camera) and having a width that is sufficient for pavement surface detection are particularly well suited for an extensive detection of the pavement extending in front of the vehicle due to their actual application as surroundings sensors or front cameras.